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ModelCap

Model decision surface

Compare AI models

Start with Jamba Large 1.7 and GPT-3.5 Turbo Instruct, or choose any two current ranked language models. Compare capability evidence, price, context, provider availability, and weight access without pretending one field decides every use case.

Current public data

Jamba Large 1.7 vs GPT-3.5 Turbo Instruct

Live dataset updated 8/17/2026, 11:13:20 PM UTC

Open 1200×630 evidence receipt
Factual comparison of Jamba Large 1.7 and GPT-3.5 Turbo Instruct
Field
ModelCap position#110#107
Index score22.522.5
EvidenceEstimatedglobal corpus prior · leave-one-anchor-out calibratedEstimatedpublisher corpus prior · leave-one-anchor-out calibrated
Input / 1M$2.00$1.50
Output / 1M$8.00$2.00
Pricing statusfreshfresh
Context256K4K
Providers11
Weight accessGated accessAPI only

Decision facts

  • Jamba Large 1.7 is #110; GPT-3.5 Turbo Instruct is #107 on the same current language board.
  • Index scores are 22.5 for Jamba Large 1.7 and 22.5 for GPT-3.5 Turbo Instruct.
  • Both positions use Estimated evidence.
  • Listed output price per 1M tokens is $8.00 for Jamba Large 1.7 and $2.00 for GPT-3.5 Turbo Instruct.
  • Published context is 256,000 tokens for Jamba Large 1.7 and 4,095 for GPT-3.5 Turbo Instruct.
  • Weight access differs: Jamba Large 1.7 is gated; GPT-3.5 Turbo Instruct is none.

These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.

Each comparison page is a permanent, shareable URL with the same live figures as this tool: ModelCap Index position, API pricing, context window, provider count, weight access and every shared benchmark board.